{"id":"W4401525591","doi":"10.1016/j.heliyon.2024.e36163","title":"Antiprotozoal peptide prediction using machine learning with effective feature selection techniques","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University Grants Commission","keywords":"Feature selection; Antiprotozoal; Machine learning; Artificial intelligence; Selection (genetic algorithm); Computer science; Feature (linguistics); Chemistry; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001744083,0.001112983,0.001336436,0.003523544,0.0003115408,0.0009157908,0.0007513787,0.0006840311,0.0008390249],"category_scores_gemma":[0.00259932,0.0002440894,0.001641822,0.002043715,0.0002626051,0.0006468802,0.0004178944,0.0007307634,0.0004591264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003962942,"about_ca_system_score_gemma":0.0008965574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001698868,"about_ca_topic_score_gemma":0.001468488,"domain_scores_codex":[0.9991136,0.0002519164,0.0001216637,0.0001944958,0.0002369698,0.00008136783],"domain_scores_gemma":[0.9985598,0.0008790032,0.0001535285,0.00004106703,0.0003350603,0.00003153852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004852334,0.0006571742,0.01638321,0.001270717,0.0005139136,0.0006888062,0.00009574462,0.140158,0.03138597,0.00124997,0.005751356,0.80136],"study_design_scores_gemma":[0.00006472878,0.000442007,0.005560665,0.0001003582,0.0002071463,0.0002633845,0.00005486675,0.9762308,0.01210107,0.001895869,0.003041399,0.00003761669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2234393,0.01061562,0.7556088,0.0008053848,0.0001675368,0.000548255,0.002446452,0.003179545,0.003189158],"genre_scores_gemma":[0.6810049,0.002488499,0.3101919,0.0003043578,0.0001601709,0.0006306565,0.00416701,0.00006392197,0.0009886337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003523544,"threshold_uncertainty_score":0.00922364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005932030019915671,"score_gpt":0.2273984859990468,"score_spread":0.2214664559791312,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}